Department of Statistics Seminar
North Carolina State University

presents

Katja Ickstadt

Department of Statistics,
University of North Carolina at Chapel Hill

"Disparate Data Resolutions in Environmental and Ecological Modeling"

ABSTRACT

Disease counts in small geographic areas are typically modeled as Poisson random variables with intensities whose logarithms are modeled by a Gaussian random field. Area-specific covariates are incorporated as additive terms in the model for the log intensities (this is known as ecological regression). However, in the conventional approach covariates are restricted to enter the model at the same spatial scale as the disease counts. Environmental covariates (e.g. pollution concentrations) are typically measured and reported at a finer resolution than are the disease counts; replacing these measurements with their averages over the larger areas may obscure local variation (and high local peaks) which could be critical for explaining the disease.

In this talk a flexible class of Bayesian hierarchical Poisson/gamma models for spatially correlated event data is used, which overcomes the problem of disparate spatial scales by relating all observable quantities to an underlying continuous random field model. Markov chain Monte Carlo methods using data augmentation are employed to estimate posterior distributions.

The models are applied to an ecological regression analysis of severe childhood wheeze and nitrogen dioxide (NO₂) pollution in Huddersfield, an English industrial town. The data are part of the SAVIAH study, an ongoing investigation into Small Area Variations In Air Quality and Health funded by the European Community. Disease counts are recorded for 427 census enumeration districts; NO₂ levels and their measurement-error variances are available at 4,500 evenly-spaced grid-points covering the region.

Friday, October 10, 1997

3:35 - 4:35 pm

206 Cox Hall

Refreshments will be served on the second floor of Dabney Hall (left of Room 222) at 3:00 pm.